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AURA's innovative approach to leveraging asymmetric annotations leads to segmentation performance that rivals traditional methods, even in the challenging context of ultra-low-field MRI.
SAM models exhibit surprisingly divergent behaviors under occlusion, with some prioritizing visible tissue and others confidently hallucinating hidden anatomy.
Stain normalization and decoupled learning can dramatically improve the robustness of white blood cell classification, even in the face of significant staining variations and class imbalances.
